Efficient Neuromorphic QUBO Solver with Spintronic Resonators

Combinatorial optimization widely exists in science and engineering problems. Some problems are NP-hard and thus become difficult to solve on edge devices due to limited power and computing resources. Quadratic Unconstrained Binary Optimization (QUBO) problem is an emerging model to approach numerous combinatorial problems. In this paper, we explore the potential of accelerating the solution of the QUBO problem using spintronic resonator arrays in combination with a neuromorphic swarm of spiking neural networks (SNN). This approach leverages the collaboration of multiple SNNs, performing a collaborative metaheuristic search that contains matrix and vector multiplication facilitated by the multiplication and accumulation (MAC) primitive based on spintronic resonator arrays. Our simulations demonstrate that the proposed method is promising in designing high-speed and energy-efficient solvers for various combinatorial optimization problems.